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Digital detection of attention and distraction behaviors

  • Paying attention helps us learn, advance in our careers, and build successful relationships, but when it’s compromised, achievement of any kind becomes far more challenging. Causes of not paying attention can range from common factors like sleep deprivation, stress, or a mood disorder to health difficulties such as ADHD, OCD, or a thyroid problem that affects concentrating. This work extracts paying attention and not paying attention behavior patterns in the context of learning. In early work, our study identified attention and distraction behaviors using gathered video recordings of online classes. The work found ten paying attention behaviors and six distracted behavior patterns. In this paper, we use computer vision techniques to extract features related to these behaviors. These features are distance between hand and face, pitch yaw roll, eye-to-camera distance, hand-to-camera distance, iris direction, gaze tracking, mouth aspect ratio, eye aspect ratio, distance between face and frame side, and facial landmark configuration. This research also applied three types of machine learning—logistic regression, decision trees, and random forest—and the accuracy rates were 79%, 86%, and 89%, respectively. This result is better than relying only on two extracted features in our previous work.

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Metadaten
Author of HS ReutlingenMartínez Madrid, Natividad
URN:urn:nbn:de:bsz:rt2-opus4-53077
DOI:https://doi.org/10.1016/j.procs.2024.09.332
ISSN:1877-0509
Erschienen in:Procedia computer science
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Conference proceeding
Language:English
Publication year:2024
Tag:attention behviour; computer vison; online sessions
Volume:246
Page Number:10
First Page:4673
Last Page:4682
DDC classes:004 Informatik
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International